Abstract
We developed a new approach to the analysis of time series based on the use of quasi-linear recurrence relations. Unlike neural networks, this approach makes it possible to explicitly obtain high-quality quasi-linear difference equations (adequately describing the considered process). Currently, we developed and tested methods for identifying the parameters of a single equation. The research considers the identification algorithm for parameters of quasilinear recurrence equation. We use it to solve the problem of regression analysis with mutually dependent observable variables, which allows to implement the generalized last deviations method (GLDM). Using this model we held the computational experiment. The model using the identified parameters allows to obtain the long-time forecast.
The work was supported by Act 211 Government of the Russian Federation, contract No. 02.A03.21.0011. The work was supported by the Ministry of Science and Higher Education of the Russian Federation (government order FENU-2020-0022).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ponce, M.: Covid 19 analitic: an R package to obtain, analyze and visualize data from the corona virus disease pandemic (2020). arXiv:2009.01091v1
Makarovskikh, T., Abotaleb, M.: Comparison between two systems for forecasting Covid-19 infected cases. IFIP Adv. Inf. Commun. Technol. 616, 107â114 (2021). https://doi.org/10.1007/978-3-030-86582-5_10
Neto, A., Ferreira, T., Batista, M., Firmino, P.: Studying the performance of cognitive models in time series forecasting. Revista de Informatica Teorica e Aplicada 27(1), 83â91 (2020). https://doi.org/10.22456/2175-2745.96181
Pan, J., Wang, H., Qiwei, Y.: Weighted least absolute deviations estimation for ARMA models with infinite variance. Economet. Theor. 23(3), 852â879 (2007)
Panchal, R., Kumar, B.: Forecasting industrial electric power consumption using regression based predictive model. Recent Trends Commun. Electron. (2021). https://doi.org/10.1201/9781003193838-26
Panyukov, A.V., Mezaal, Y.A.: Stable estimation of autoregressive model parameters with exogenous variables on the basis of the generalized least absolute deviation method. IFAC-PapersOnLine 51(11), 1666â1669 (2018). https://doi.org/10.1016/j.ifacol.2018.08.217. Open access
Panyukov, A.V., Mezaal, Y.A.: Improving of the identification algorithm for a quasilinear recurrence equation. In: Olenev, N., Evtushenko, Y., Khachay, M., Malkova, V. (eds.) OPTIMA 2020. CCIS, vol. 1340, pp. 15â26. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65739-0_2
Panyukov, A.: Scalability of algorithms for arithmetic operations in radix notation. Reliable Comput. 19, 417â434 (2015). http://interval.louisiana.edu/reliable-computing-journal/volume-19/reliable-computing-19-pp-417-434.pdf
Panyukov, A., Mezaal, Y.: Stable identification of linear autoregressive model with exogenous variables on the basis of the generalized least absolute deviation method. Bull. South Ural State Univ. Ser. Math. Model. Program. Comput. Softw. 11(1), 35â43 (2018). https://doi.org/10.14529/mmp180104
Panyukov, A., Tyrsin, A.: Stable parametric identification of vibratory diagnostics objects. J. Vibroeng. 10(2), 142â146 (2008). https://www.extrica.com/article/10181
Sirotin, D.: Neural network approach to forecasting the cost of ferroalloy products. Izvestiya. Ferrous Metall. 63(1), 78â83 (2020). https://doi.org/10.17073/0368-0797-2020-1-78-83
Tyrsin, A.N.: Robust construction of regresson models based on the least absolute deviations method. J. Math. Sci. 139(3), 6634â6642 (2006). https://doi.org/10.1007/s10958-006-0380-7
Yakubova, D.: Econometric models of development and forecasting of black metallurgy of Uzbekistan. Asian J. Multidimensional Res. (AJMR) 8, 310â314 (2019). https://doi.org/10.5958/2278-4853.2019.00205.2
Abotaleb M.: GLDM-model (2022). https://github.com/abotalebmostafa11/GLDM-model
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
Âİ 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Panyukov, A., Makarovskikh, T., Abotaleb, M. (2022). Forecasting with Using Quasilinear Recurrence Equation. In: Olenev, N., Evtushenko, Y., JaÄimoviÄ, M., Khachay, M., Malkova, V., Pospelov, I. (eds) Advances in Optimization and Applications. OPTIMA 2022. Communications in Computer and Information Science, vol 1739. Springer, Cham. https://doi.org/10.1007/978-3-031-22990-9_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-22990-9_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22989-3
Online ISBN: 978-3-031-22990-9
eBook Packages: Computer ScienceComputer Science (R0)